{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "from matplotlib import gridspec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [*] Building a NTM model\n",
      "Percent: [####################] 100.00% Finished.\n",
      " [*] Build a NTM model finished\n",
      " [*] Reading checkpoints...\n"
     ]
    }
   ],
   "source": [
    "from tasks import *\n",
    "\n",
    "input_dim=10\n",
    "output_dim=10\n",
    "\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "cell = NTMCell(input_dim=input_dim, output_dim=output_dim)\n",
    "ntm = NTM(cell, sess, 1, 10, 100, forward_only=True)\n",
    "\n",
    "ntm.load('../checkpoint', 'copy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " true output : \n",
      "  ##  #   \n",
      "  #### ## \n",
      "   #######\n",
      "  # ## # #\n",
      "    # ## #\n",
      " predicted output :\n",
      "  ##  #   \n",
      "  #### ## \n",
      "   #######\n",
      "  # ## # #\n",
      "    # ## #\n",
      " Loss : 0.000001\n"
     ]
    }
   ],
   "source": [
    "copy(ntm, 5, sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot(ntm, seq_length, sess):\n",
    "    seq, outputs, read_w, write_w, loss = copy(ntm, seq_length, sess, print_=False)\n",
    "    read_w[0] = np.squeeze(read_w[0])\n",
    "    write_w[0] = np.squeeze(write_w[0])\n",
    "\n",
    "    shape2 = list(np.array(outputs).shape)\n",
    "    shape2[0] += 2\n",
    "\n",
    "    shape3 = list(np.array(read_w).shape)\n",
    "    shape3[0] += 2\n",
    "\n",
    "    z1 = np.zeros_like(outputs)\n",
    "    z2 = np.zeros(shape2)\n",
    "\n",
    "    s1 = np.zeros_like(seq[0]); s1[0]=1\n",
    "    s2 = np.zeros_like(seq[0]); s2[1]=1\n",
    "\n",
    "    seq = [s1] + seq + [s2]\n",
    "\n",
    "    seq = np.r_[np.array(seq), z1]\n",
    "    outputs = np.r_[z2, outputs]\n",
    "\n",
    "    if seq_length >= 80:\n",
    "        fig = plt.figure(1,figsize=(20,16))\n",
    "        gs = gridspec.GridSpec(4, 1, height_ratios=[0.4, 0.4, 1.6, 1.6])\n",
    "    elif seq_length >= 60:\n",
    "        fig = plt.figure(1,figsize=(20,14))\n",
    "        gs = gridspec.GridSpec(4, 1, height_ratios=[0.6, 0.6, 1.4, 1.4])\n",
    "    elif seq_length >= 50:\n",
    "        fig = plt.figure(1,figsize=(20,14))\n",
    "        gs = gridspec.GridSpec(4, 1, height_ratios=[0.8, 0.8, 1.2, 1.2])\n",
    "    elif seq_length >= 20:\n",
    "        fig = plt.figure(1,figsize=(20,14))\n",
    "        gs = gridspec.GridSpec(4, 1, height_ratios=[0.9, 0.9, 1.1, 1.1])\n",
    "    else:\n",
    "        fig = plt.figure(1,figsize=(20,10))\n",
    "        gs = gridspec.GridSpec(4, 1, height_ratios=[1, 1, 1, 1])\n",
    "\n",
    "    ax0 = plt.subplot(gs[0])\n",
    "    ax0.imshow(seq.T, interpolation='nearest')\n",
    "    ax0.set_ylabel('input')\n",
    "    \n",
    "    ax1 = plt.subplot(gs[1])\n",
    "    ax1.imshow(outputs.T, interpolation='nearest')\n",
    "    ax1.set_xlabel('time')\n",
    "    ax1.set_ylabel('output')\n",
    "    \n",
    "    ax2 = plt.subplot(gs[2])\n",
    "    ax2.imshow(read_w[1:-1], cmap='Greys', interpolation='nearest')\n",
    "    ax2.set_xlabel('write weight')\n",
    "    ax2.set_ylabel('time')\n",
    "    \n",
    "    ax3 = plt.subplot(gs[3])\n",
    "    ax3.imshow(read_w[1:-1], cmap='Greys', interpolation='nearest')\n",
    "    ax3.set_xlabel('read weight')\n",
    "    ax3.set_ylabel('time')\n",
    "\n",
    "    print(\"Loss : %f\" % loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss : 0.000000\n"
     ]
    },
    {
     "data": {
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Cuvv+qnp9ksuy+o9B53f3DSOXBQDACIRBADAR3f3HSZ41dh0AAIzLMTEAAACACREGAQAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f35434d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(ntm, 5, sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss : 0.000003\n"
     ]
    },
    {
     "data": {
      "image/png": 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GloYOMICloQMAwCBcQBoADmt13fZS/PIIJPsnNwBYTMogAMbotiSPWXf/5MnYISxPPw2w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\nBkmSJEmSJE2R/wdJomGUnIhjRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3b40cc290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(ntm, 10, sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss : 0.000009\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAAAABkERAAAAAEmSpVkXAAAAACyWqtrt4929RZWwt1lR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3b1ccf410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(ntm, 20, sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss : 0.000024\n"
     ]
    },
    {
     "data": {
      "image/png": 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3BbNt+hgjvTbJu7v7SUmemuTmTNDHDmzYTnJBkk919y3d/bUkb05y8cQ1seS6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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3f862f990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(ntm, 30, sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss : 0.000257\n"
     ]
    },
    {
     "data": {
      "image/png": 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uvr+7/yLGGgzhQUkeVlVHkzw0yT0x1mDPuvsPk/z5Kc07ja3Lkrxt9vvueJJP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3f9201450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(ntm, 50, sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss : 0.029731\n"
     ]
    },
    {
     "data": {
      "image/png": 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b26y0yf+2fV93vyFJuvu+7v58jG3Ww4OSPKyqjiR5aJJ7Ymyzgrr795P8xQmb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\nDAIAAAAYkf8P42NjBXyBiJUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3f9bd7510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(ntm, 60, sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss : 0.035636\n"
     ]
    },
    {
     "data": {
      "image/png": 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UJD+a5NbIOQZSVY9N8q+SPL27fyTJqUleGDnH7rsyq78rrLVhnlXVDyZ5QZKn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3fb5bcfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(ntm, 70, sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss : 0.002402\n"
     ]
    },
    {
     "data": {
      "image/png": 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kn0lyfZJLu/u2pRYGAABwCswMApjchUk+2d13dPdXk7wjycVLrgkAAOCUnLns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\nABiIMAgAAABgIMIgAAAAgIH8/8iOGI0gwjqoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3fb4bf910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(ntm, 80, sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss : 1113.521484\n"
     ]
    },
    {
     "data": {
      "image/png": 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ynRB6bXe/ZbT7QN+jrEpS6IYkj6+qx1TVA5NcnuStS44JOCSq6sGjDHqq6iFJ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\nEAAAAMCAhEIAAAAAA/r/GMHn38co8HAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3fe14fc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(ntm, 100, sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
